ZeusDB Vector Database
A high performance vector database for AI workloads. Find relevant results fast, keep latency low as you scale, and move from prototype to production with confidence.
Start fast. Tune deep. Scale with confidence.
A modern vector database that delivers relevant results quickly and grows with your workload.
High performance engine
Optimized core for low latency and predictable throughput as users and datasets grow.
ANN search with HNSW
Fast, accurate similarity search across high dimensional embeddings using proven HNSW graphs.
Product Quantization (PQ)
Compress vectors for big gains in memory efficiency while keeping search quality strong.
Developer-friendly API
Add vectors, run searches, and filter by metadata with simple, consistent methods.
Flexible inputs
Works with native types and array formats so you can plug in existing pipelines without rework.
Metadata filtering
Return only what matters with precise, contextual filters that mirror your domain.
Persistence built in
Save and reload complete indexes, metadata, and quantized vectors across environments.
Observability that helps
Structured logs and useful metrics give teams clarity for planning, troubleshooting, and tuning.
Turn vector search into product impact
ZeusDB adds fast, relevant retrieval to your stack so teams can prototype, iterate, and ship with confidence, without reworking how you build.
Connect ZeusDB to your workflow
Use what you already know today and expand as your needs grow. More integrations are on the way.
LangChain
AvailablePlug ZeusDB into LangChain workflows for RAG, tools, and retrieval. Ship fast without rearchitecting.
LlamaIndex
In developmentAdvanced RAG pipelines made simple. Use LlamaIndex document processing with the ZeusDB vector database for production performance.
Hugging Face
In developmentOpen source models with production retrieval. Pair Hugging Face embeddings with ZeusDB for local and accelerated deployments.
Anthropic
In developmentEnterprise grade RAG with Claude. Use Anthropic reasoning models with ZeusDB for fast retrieval, persistence, and safety controls.
OpenAI
In developmentBuild production RAG quickly by combining OpenAI embeddings with ZeusDB for low latency and persistent indexing.
Semantic Kernel
In developmentAdd durable AI memory to apps in the Microsoft ecosystem. Use Semantic Kernel planners with ZeusDB for reliable retrieval.
Pick the right path for your needs
Includes:
- High performance core
- Python API
- Approximate nearest neighbor search
- Optional product quantization
- Persistence and metadata filters
- Community support
Everything in Open Source, plus:
- Managed hosting
- SSO and RBAC
- Audit trails and encryption
- Automated backups and restore
- Observability and SLAs
- Priority support
Mini FAQ
Clear answers to the most common questions from teams looking at vector databases.
What is a Vector Database?
A database built to find the closest items in high-dimensional space. It powers semantic search, RAG, recommendations, and other AI features that rely on embeddings.
What is a Vector Store?
A vector store is a lightweight way to hold embeddings and run basic similarity search, often in memory or as a thin wrapper over another datastore. Good for prototypes and small apps.
How is ZeusDB different?
Fast to adopt with smart defaults, and powerful to tune as you scale. You get predictable performance, metadata filtering, and optional compression without reworking your stack.
Is ZeusDB open source?
Yes. The core ZeusDB Vector Database module is open source and free to use. A managed enterprise edition is in development to add features like SSO, RBAC, audit trails, backups, encryption, and SLAs.
Which parts are open source?
Today, the core vector database module covering indexing, search, compression, and persistence is open source. Advanced enterprise capabilities will be offered in the managed edition.
Does it fit my workflow?
Yes. ZeusDB works alongside common pipelines and tooling so teams can add vector retrieval without changing how they ship software.
How does it scale?
Built for low latency and consistent behavior as data and traffic grow. Techniques like efficient indexing and optional vector compression help performance and memory footprint.
What about security and operations?
Structured logs, useful metrics, and persistence help with day-two operations. The upcoming cloud adds SSO, RBAC, audit trails, and automated backups.